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undefinedundefineddef analyze_test_coverage(self):
"""Comprehensive test coverage analysis with gap identification"""
coverage_stats = {
'line_coverage': self.test_results['coverage']['lines']['pct'],
'branch_coverage': self.test_results['coverage']['branches']['pct'],
'function_coverage': self.test_results['coverage']['functions']['pct'],
'statement_coverage': self.test_results['coverage']['statements']['pct']
}
# Identify coverage gaps
uncovered_files = self.test_results['coverage']['files']
gap_analysis = []
for file_path, file_coverage in uncovered_files.items():
if file_coverage['lines']['pct'] < 80:
gap_analysis.append({
'file': file_path,
'coverage': file_coverage['lines']['pct'],
'risk_level': self._assess_file_risk(file_path, file_coverage),
'priority': self._calculate_coverage_priority(file_path, file_coverage)
})
return coverage_stats, gap_analysis
def analyze_failure_patterns(self):
"""Statistical analysis of test failures and pattern identification"""
failures = self.test_results['failures']
# Categorize failures by type
failure_categories = {
'functional': [],
'performance': [],
'security': [],
'integration': []
}
for failure in failures:
category = self._categorize_failure(failure)
failure_categories[category].append(failure)
# Statistical analysis of failure trends
failure_trends = self._analyze_failure_trends(failure_categories)
root_causes = self._identify_root_causes(failures)
return failure_categories, failure_trends, root_causes
def predict_defect_prone_areas(self):
"""Machine learning model for defect prediction"""
# Prepare features for prediction model
features = self._extract_code_metrics()
historical_defects = self._load_historical_defect_data()
# Train defect prediction model
X_train, X_test, y_train, y_test = train_test_split(
features, historical_defects, test_size=0.2, random_state=42
)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Generate predictions with confidence scores
predictions = model.predict_proba(features)
feature_importance = model.feature_importances_
return predictions, feature_importance, model.score(X_test, y_test)
def assess_release_readiness(self):
"""Comprehensive release readiness assessment"""
readiness_criteria = {
'test_pass_rate': self._calculate_pass_rate(),
'coverage_threshold': self._check_coverage_threshold(),
'performance_sla': self._validate_performance_sla(),
'security_compliance': self._check_security_compliance(),
'defect_density': self._calculate_defect_density(),
'risk_score': self._calculate_overall_risk_score()
}
# Statistical confidence calculation
confidence_level = self._calculate_confidence_level(readiness_criteria)
# Go/No-Go recommendation with reasoning
recommendation = self._generate_release_recommendation(
readiness_criteria, confidence_level
)
return readiness_criteria, confidence_level, recommendation
def generate_quality_insights(self):
"""Generate actionable quality insights and recommendations"""
insights = {
'quality_trends': self._analyze_quality_trends(),
'improvement_opportunities': self._identify_improvement_opportunities(),
'resource_optimization': self._recommend_resource_optimization(),
'process_improvements': self._suggest_process_improvements(),
'tool_recommendations': self._evaluate_tool_effectiveness()
}
return insights
def create_executive_report(self):
"""Generate executive summary with key metrics and strategic insights"""
report = {
'overall_quality_score': self._calculate_overall_quality_score(),
'quality_trend': self._get_quality_trend_direction(),
'key_risks': self._identify_top_quality_risks(),
'business_impact': self._assess_business_impact(),
'investment_recommendations': self._recommend_quality_investments(),
'success_metrics': self._track_quality_success_metrics()
}
return reportundefineddef analyze_test_coverage(self):
"""Comprehensive test coverage analysis with gap identification"""
coverage_stats = {
'line_coverage': self.test_results['coverage']['lines']['pct'],
'branch_coverage': self.test_results['coverage']['branches']['pct'],
'function_coverage': self.test_results['coverage']['functions']['pct'],
'statement_coverage': self.test_results['coverage']['statements']['pct']
}
# Identify coverage gaps
uncovered_files = self.test_results['coverage']['files']
gap_analysis = []
for file_path, file_coverage in uncovered_files.items():
if file_coverage['lines']['pct'] < 80:
gap_analysis.append({
'file': file_path,
'coverage': file_coverage['lines']['pct'],
'risk_level': self._assess_file_risk(file_path, file_coverage),
'priority': self._calculate_coverage_priority(file_path, file_coverage)
})
return coverage_stats, gap_analysis
def analyze_failure_patterns(self):
"""Statistical analysis of test failures and pattern identification"""
failures = self.test_results['failures']
# Categorize failures by type
failure_categories = {
'functional': [],
'performance': [],
'security': [],
'integration': []
}
for failure in failures:
category = self._categorize_failure(failure)
failure_categories[category].append(failure)
# Statistical analysis of failure trends
failure_trends = self._analyze_failure_trends(failure_categories)
root_causes = self._identify_root_causes(failures)
return failure_categories, failure_trends, root_causes
def predict_defect_prone_areas(self):
"""Machine learning model for defect prediction"""
# Prepare features for prediction model
features = self._extract_code_metrics()
historical_defects = self._load_historical_defect_data()
# Train defect prediction model
X_train, X_test, y_train, y_test = train_test_split(
features, historical_defects, test_size=0.2, random_state=42
)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Generate predictions with confidence scores
predictions = model.predict_proba(features)
feature_importance = model.feature_importances_
return predictions, feature_importance, model.score(X_test, y_test)
def assess_release_readiness(self):
"""Comprehensive release readiness assessment"""
readiness_criteria = {
'test_pass_rate': self._calculate_pass_rate(),
'coverage_threshold': self._check_coverage_threshold(),
'performance_sla': self._validate_performance_sla(),
'security_compliance': self._check_security_compliance(),
'defect_density': self._calculate_defect_density(),
'risk_score': self._calculate_overall_risk_score()
}
# Statistical confidence calculation
confidence_level = self._calculate_confidence_level(readiness_criteria)
# Go/No-Go recommendation with reasoning
recommendation = self._generate_release_recommendation(
readiness_criteria, confidence_level
)
return readiness_criteria, confidence_level, recommendation
def generate_quality_insights(self):
"""Generate actionable quality insights and recommendations"""
insights = {
'quality_trends': self._analyze_quality_trends(),
'improvement_opportunities': self._identify_improvement_opportunities(),
'resource_optimization': self._recommend_resource_optimization(),
'process_improvements': self._suggest_process_improvements(),
'tool_recommendations': self._evaluate_tool_effectiveness()
}
return insights
def create_executive_report(self):
"""Generate executive summary with key metrics and strategic insights"""
report = {
'overall_quality_score': self._calculate_overall_quality_score(),
'quality_trend': self._get_quality_trend_direction(),
'key_risks': self._identify_top_quality_risks(),
'business_impact': self._assess_business_impact(),
'investment_recommendations': self._recommend_quality_investments(),
'success_metrics': self._track_quality_success_metrics()
}
return reportundefinedundefinedundefinedundefinedundefined